Protein expression-based classifier for prediction of recurrence in adenocarcinoma

Abstract

A method for making a prognosis for a patient afflicted with a type of cancer which comprises (a) determining in a sample of a tumor from the patient a level of expression for at least three biomarkers from the following group: thyroid transcription factor-1 (TTF1), signal transducer and activator of transcription-3 (STAT-3), beta-catenin and cyclin D1; (b) calculating a score based on the levels of expression determined in step (a); (c) correlating the score obtained in step (b) with a series of predetermined reference scores associated with a series of prognoses; so as to thereby make a prognosis for the patient.

Description

[0001]This application claims priority of U.S. Provisional Application No. 61 / 463,715, filed Feb. 22, 2011, the entire content of which is hereby incorporated by reference into this application.[0002]This invention was made with support under Grant No. 2009-0097 from the Connecticut Department of Health.[0003]Throughout this application, various publications are referenced by footnotes and / or parentheses. The disclosures of each of the publications found in this specification is hereby incorporated by reference into this application in order to more fully describe the state of the art as known to those skilled therein as of the date of this application.FIELD OF THE INVENTION[0004]This invention relates to the field of predicting recurrences of adenocarcinoma and stratifying low stage lung adenocarcinoma patients into groups that will or will not benefit from adjuvant therapy.BACKGROUND OF THE INVENTION[0005]Lung cancer, predominantly nonsmall-cell lung cancer (NSCLC), is the most co...

AI Technical Summary

Benefits of technology

The invention provides a method for making a prognosis for a patient with cancer by measuring the levels of expression of certain biomarkers in a sample of the tumor from the patient. These biomarkers include thyroid transcription factor-1 (TTF1), signal transducer and activator of transcription-3 (STAT-3), beta-catenin, and cyclin D1. By calculating a score based on the levels of expression and comparing it with a series of predetermined reference scores, a prognosis can be made for the patient. The method can also be used to classify a patient as being at a low or high risk for recurrence of cancer. The invention provides a more accurate way to predict the likelihood of cancer recurrence and can help guide treatment decisions.

Problems solved by technology

Early stage treatment includes surgical resection but no adjuvant chemotherapy, resulting in poor overall 5 year survival rate, generally due to recurrence of disease (Mountain CF.

IHC is generally poorly standardized and results are typically evaluated by eye in a subjective manner.

Loss of beta catenin is associated with poor prognosis in lung cancer patients.

A critical clinical problem in management of adenocarcinoma of the lung is to determine which low stage patients are cured by surgery alone, versus which will benefit from adjuvant chemotherapy.

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